The present invention relates to the field of object detection and, more particularly, to an improved object detection approach using an ensemble classifier.
Object detection is a growing field for image processing systems. Accuracy and speed or computational cost are the typical trade-offs in the approaches used. That is, higher accuracy of detection means more comparisons and/or more candidates, which increases the time and resources required by the process.
Because of this balance, it has become popular to reduce search costs using an ensemble classifier and a cascaded approach to eliminate candidates based on subsets of weak classifiers, without unfolding the entire ensemble classifier. This type of approach, such as the multiple instance pruning (MIP) algorithm, uses the minimum score of all ground truth candidates observed across training images and detected by the ensemble classifier to eliminate candidates in each cascade stage. Use of the lower bound, minimum score, as the rejection threshold of a stage still allows a certain number of negative candidates to be accepted, which actually increases the computational cost without increasing accuracy.